A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data. Issue 6 (2nd June 2020)

• Record Type: Journal Article
• Authors: Wu, Lun; Cheng, Ximeng; Kang, Chaogui; Zhu, Di; Huang, Zhou; Liu, Yu
• Is Part Of: International journal of digital earth. Volume 13:Issue 6(2020)
• Page Start: 708
• Page End: 726
• Publication Date: 2020-06-02
• Subjects: Mixed use -- spatial–temporal pattern -- urban function -- human activity -- big geo-data; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2018.1556353
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Mixed use has been extensively applied as an urban planning principle and hinders the study of single urban functions. To address this problem, it is worth decomposing the mixed use. Inspired by the concept of spectral unmixing in remote sensing applications, this paper proposes a framework for mixed-use decomposition based on big geo-data. Mixed-use decomposition in terms of human activities differs from traditional land use research, and it is more reasonable to infer the actual urban function of land. The framework consists of four steps, namely temporal activity signature extraction, urban function base curve extraction, mixed-use decomposition, and result validation . First, the temporal activity signatures (TASs) of each zone are extracted as the proxy of human activity patterns. Second, the diurnal TASs of routine activities are extracted as urban function base curves (i.e. endmembers). Third, a linear decomposition model is used to decompose the mixed use and obtain multiple results (urban function composition, dynamic activity proportions, and the mixing index). Finally, result validation strategies are concluded. This framework offers method extensibility and has few requirements for the input data. It is validated by means of a case study of Beijing, based on a social media check-in dataset.